Name
Title | ||
|---|---|---|
Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction. |
Abstract | ||
|---|---|---|
•We explore the effects of sample types on the predictive performance of ensembles.•We focus on credit risk and corporate bankruptcy prediction problems.•We characterize the databases based on the positive sample types.•We show that performance depends on the prevalent type of positive samples. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.inffus.2018.07.004 | Information Fusion |
Keywords | Field | DocType |
Types of samples,Credit risk,Bankruptcy,Classifier ensemble,Imbalance | Ensembles of classifiers,AdaBoost,Binary classification,Bankruptcy prediction,Artificial intelligence,Random forest,Classifier (linguistics),Machine learning,Credit risk,Mathematics,Gradient boosting | Journal |
Volume | ISSN | Citations |
47 | 1566-2535 | 2 |
PageRank | References | Authors |
0.37 | 47 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vicente García | 1 | 78 | 6.37 |
| A. I. Marqués | 2 | 209 | 10.40 |
| J. Salvador Sánchez | 3 | 139 | 14.01 |